{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# visualize model outputs on randomly generated test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import torch\n",
    "import torch.backends.cudnn as cudnn\n",
    "import torchvision.transforms as transforms\n",
    "from munch import Munch\n",
    "\n",
    "import models\n",
    "import utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "oil_change_data_root = '../data/OilChangeDataset'\n",
    "ann_file = '20171103_OilChange.json'\n",
    "camera_name = 'floating_kinect1'\n",
    "mode = 'object'\n",
    "#mode = 'mask'\n",
    "config_path = 'pretrained/floating_kinect1_{}/config.yml'.format(mode)\n",
    "blender_path = '/usr/local/bin/blender'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(config_path, 'r') as f:\n",
    "    cfg = Munch.fromYAML(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading annotations into memory...\n",
      "Done (t=0.53s)\n",
      "creating index...\n",
      "index created!\n",
      "loading annotations into memory...\n",
      "Done (t=0.47s)\n",
      "creating index...\n",
      "index created!\n"
     ]
    }
   ],
   "source": [
    "camera_parameters = utils.get_camera_parameters(oil_change_data_root, ann_file, camera_name)\n",
    "model_paths = utils.get_model_paths(oil_change_data_root, ann_file, cfg.data.objects)\n",
    "pose_renderers = [\n",
    "    utils.PoseRenderer(blender_path, camera_parameters, model_path, mode)\n",
    "    for model_path in model_paths\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=> loaded checkpoint 'pretrained/floating_kinect1_object/checkpoint_00002100.pth.tar' (epoch 2100)\n"
     ]
    }
   ],
   "source": [
    "model = models.Model(cfg.arch)\n",
    "model = torch.nn.DataParallel(model).cuda()\n",
    "cudnn.benchmark = True\n",
    "checkpoint = torch.load(cfg.training.resume)\n",
    "model.load_state_dict(checkpoint['state_dict'])\n",
    "model.eval()\n",
    "print(\"=> loaded checkpoint '{}' (epoch {})\".format(cfg.training.resume, checkpoint['epoch']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "transform = transforms.ToTensor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def visualize_batch(model, pose_renderers, input, object_index):\n",
    "    object_index = object_index.cuda(non_blocking=True)\n",
    "    position, orientation = model(input, object_index)\n",
    "\n",
    "    image = input.numpy()\n",
    "    image = image.squeeze(1) if image.shape[1] == 1 else image.transpose((0, 2, 3, 1))\n",
    "    object_index = object_index.cpu().numpy()\n",
    "    position = position.cpu().numpy()\n",
    "    orientation = orientation.cpu().numpy()\n",
    "\n",
    "    for i in range(input.size(0)):\n",
    "        _, axes = plt.subplots(1, 2, figsize=(2 * 4, 4))\n",
    "        rendered_pose = pose_renderers[object_index[i]].render(position[i], orientation[i])\n",
    "        axes[0].imshow(image[i], cmap='gray' if image.ndim == 3 else None)\n",
    "        axes[1].imshow(rendered_pose, cmap='gray' if image.ndim == 3 else None)\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# randomly generate poses, render poses as input images, then display (input image, rendered pose estimate)\n",
    "num_examples = 8\n",
    "object_scales = [0.3, 0.3, 0.2, 0.7, 0.2]\n",
    "\n",
    "with torch.no_grad():\n",
    "    for _ in range(num_examples):\n",
    "        # randomly sample an object class with random pose\n",
    "        object_index = np.random.randint(len(cfg.data.objects))\n",
    "        object_scale = object_scales[object_index]\n",
    "        position_mean = [0, 0, object_scale * 3]\n",
    "        position_cov = np.diag(np.square(object_scale * 0.5 * np.array([1.5, 0.8, 2])))\n",
    "        position = np.random.multivariate_normal(position_mean, position_cov)\n",
    "        orientation = np.random.randn(4)\n",
    "        orientation = np.divide(orientation, np.linalg.norm(orientation, axis=0, keepdims=True))\n",
    "        rendered_pose = pose_renderers[object_index].render(position, orientation)\n",
    "        input = transform(rendered_pose).unsqueeze(0)\n",
    "        object_index = torch.LongTensor([object_index])\n",
    "        visualize_batch(model, pose_renderers, input, object_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
